Codifying the Judge: Scalable Evaluation via Program Distillation

Abstract

LLM-as-a-judge has become the standard for automated evaluation, but it suffers from high cost, inference latency, and opaque decisions---limitations that undermine its scalability and reliability. We address these with a simple, efficient alternative: program distillation. Instead of prompting an LLM at evaluation time, we distill its decision logic into a committee of programs that can score candidates directly. These programmatic judges offer transparency, are easily inspected or edited, and eliminate per-sample API costs. Building on this notion, we introduce PAJAMA, a system that synthesizes programs as judges, aggregates their decisions into a joint verdict, and incorporates a fallback mechanism to selectively escalate low-confidence cases to an LLM. Across five datasets and eight model families, we show that programmatic judges match the performance of a 13B-size LLM judge at 47x higher throughput. When using program outputs as routing signals, PAJAMA improves both accuracy and throughput and advances the Pareto frontier. Beyond evaluation, programmatic judges produce cheap and effective reward signals: on RewardBench, a reward model distilled from programs'verdicts outperforms one trained on a proprietary LLM's labels at two orders of magnitude lower API cost.